Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

4 papers of 8,653Sort Recent · Most cited
  1. 2024
    Category Adaptation Meets Projected Distillation in Generalized Continual Category DiscoveryGrzegorz Rypeść, Daniel Marczak, Sebastian Cygert … Bartłomiej TwardowskiECCV · Warsaw University of Technology · Gdańsk University of Technology · +1
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  2. 2024
    Divide and not forget: Ensemble of selectively trained experts in Continual LearningGrzegorz Rypeść, Sebastian Cygert, Valeriya Khan … Bartłomiej TwardowskiICLR · Warsaw University of Technology · Integrated Detector Electronics AS (Norway) · +6
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  3. 2023
    Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual LearningFilip Szatkowski, Mateusz Pyla, Marcin Przewięźlikowski … T. P. TrzcinskiICCV · Warsaw University of Technology · Integrated Detector Electronics AS (Norway) · +5
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  4. 2023
    Looking through the past: better knowledge retention for generative replay in continual learningValeriya Khan, Sebastian Cygert, Bartłomiej Twardowski, T. P. TrzcinskiICCV · Integrated Detector Electronics AS (Norway) · Corporación Universitaria de Colombia Ideas · +4
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About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.